Currently, a human player (“player”) may a video game such as a basketball game against a computer. The computer may be referred to as a CPU player. As the game is played, the player may enter input via a game controller to cause an animated agent to perform actions. None of the input is recorded, however. The computer merely responds to the input. The CPU player has been programmed with a relatively robust set of default responses from which to apply in reaction to the player input. Over time, the player may come to recognize the CPU player responses and adapt to overcome them. Once this happens, the player does not have the same appreciation for the video game and may stop playing because the challenge of winning is gone.
Thus, current video game systems do not adaptively learn a player's tendencies and moves when faced with a situation while playing a video game. Some sequences of moves are more successful than others. A system that could record and analyze player moves in contextual conjunction with an opponent or a defense to those moves and use the knowledge gained in subsequent games may result in a more interesting and challenging video game experience for players.
In this way, a CPU player may also learn the player's moves and use them in addition to the default set of responses to game situations. This would make it more difficult for the player to anticipate the moves of a CPU player thereby extending the challenge of the video game.
It is with respect to these and other considerations that the present improvements have been needed.